際際滷shows by User: ruthgarcia4 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: ruthgarcia4 / Wed, 26 Sep 2018 10:09:22 GMT 際際滷Share feed for 際際滷shows by User: ruthgarcia4 Using Simple Machine Learning Models in a New Ads Manager /slideshow/using-simple-machine-learning-models-in-a-new-ads-manager-116658995/116658995 rgpresentationdatasciencesummit2018meetup-180926100922
Presentation on a simple click prediction model. Learnings from a DS project and Engineering team. Meetup at Skyscanner 2018]]>
Presentation on a simple click prediction model. Learnings from a DS project and Engineering team. Meetup at Skyscanner 2018]]> Wed, 26 Sep 2018 10:09:22 GMT /slideshow/using-simple-machine-learning-models-in-a-new-ads-manager-116658995/116658995 ruthgarcia4@slideshare.net(ruthgarcia4) Using Simple Machine Learning Models in a New Ads Manager ruthgarcia4 Presentation on a simple click prediction model. Learnings from a DS project and Engineering team. Meetup at Skyscanner 2018 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rgpresentationdatasciencesummit2018meetup-180926100922-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation on a simple click prediction model. Learnings from a DS project and Engineering team. Meetup at Skyscanner 2018
Using Simple Machine Learning Models in a New Ads Manager from Ruth Garcia Gavilanes
]]> 99 1 https://cdn.slidesharecdn.com/ss_thumbnails/rgpresentationdatasciencesummit2018meetup-180926100922-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Using Simple Machine Learning Models in a New Ads Manager /slideshow/using-simple-machine-learning-models-in-a-new-ads-manager/114426333 rgpresentationdssummit2018-180914074055
The data science summit (AI Conference) Olympia London https://www.thedatasciencesummit.com/]]>
The data science summit (AI Conference) Olympia London https://www.thedatasciencesummit.com/]]> Fri, 14 Sep 2018 07:40:55 GMT /slideshow/using-simple-machine-learning-models-in-a-new-ads-manager/114426333 ruthgarcia4@slideshare.net(ruthgarcia4) Using Simple Machine Learning Models in a New Ads Manager ruthgarcia4 The data science summit (AI Conference) Olympia London https://www.thedatasciencesummit.com/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rgpresentationdssummit2018-180914074055-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The data science summit (AI Conference) Olympia London https://www.thedatasciencesummit.com/
Using Simple Machine Learning Models in a New Ads Manager from Ruth Garcia Gavilanes
]]> 53 1 https://cdn.slidesharecdn.com/ss_thumbnails/rgpresentationdssummit2018-180914074055-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Using Machine Learning in the delivery of ads /slideshow/using-machine-learning-in-the-delivery-of-ads/102643380 rgpresentationndrjune2018pdf-180619085349
In this presentation, I explained that a simple machine learning model can be implemented in some situations even if we do not have the perfect conditions. We go over the first iteration of a click prediction model used in the delivery of CPC ads. ]]>
In this presentation, I explained that a simple machine learning model can be implemented in some situations even if we do not have the perfect conditions. We go over the first iteration of a click prediction model used in the delivery of CPC ads. ]]> Tue, 19 Jun 2018 08:53:49 GMT /slideshow/using-machine-learning-in-the-delivery-of-ads/102643380 ruthgarcia4@slideshare.net(ruthgarcia4) Using Machine Learning in the delivery of ads ruthgarcia4 In this presentation, I explained that a simple machine learning model can be implemented in some situations even if we do not have the perfect conditions. We go over the first iteration of a click prediction model used in the delivery of CPC ads. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rgpresentationndrjune2018pdf-180619085349-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In this presentation, I explained that a simple machine learning model can be implemented in some situations even if we do not have the perfect conditions. We go over the first iteration of a click prediction model used in the delivery of CPC ads.
Using Machine Learning in the delivery of ads from Ruth Garcia Gavilanes
]]> 331 3 https://cdn.slidesharecdn.com/ss_thumbnails/rgpresentationndrjune2018pdf-180619085349-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Assessing Online Ads Beyond Only Clicks /slideshow/data-scienceonads/75459860 datascienceonads-170427085955
Plan to implement quality of ads science in Skyscanner]]>
Plan to implement quality of ads science in Skyscanner]]> Thu, 27 Apr 2017 08:59:55 GMT /slideshow/data-scienceonads/75459860 ruthgarcia4@slideshare.net(ruthgarcia4) Assessing Online Ads Beyond Only Clicks ruthgarcia4 Plan to implement quality of ads science in Skyscanner <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datascienceonads-170427085955-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Plan to implement quality of ads science in Skyscanner
Assessing Online Ads Beyond Only Clicks from Ruth Garcia Gavilanes
]]> 173 2 https://cdn.slidesharecdn.com/ss_thumbnails/datascienceonads-170427085955-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 An Analysis of Human-Generated Friendship Recommendations /slideshow/an-analysis-of-humangenerated-friendship-recommendations/69091993 ffproject-161116122624
Online social networks support users in a wide range of activities, such as sharing information and making recommendations. In this talk, I will talk about a human-generated recommendations of friendship based on hashtags that emerged in Twitter around 2010. I will present a study of a large-scale corpus of human friendship recommendations, using a large corpus of tweets gathered over a 24 week period and involving a set of nearly 6 million users. I will show that these explicit recommendations had a measurable effect on the process of link creation, increasing the chance of link creation between two and three times on average, compared with a recommendation-free scenario. Also, ties created after such recommendations have up to 6% more longevity than other Twitter ties. Finally, I will talk about a supervised system that ranks our user-generated recommendations, surfacing the most valuable ones with high precision (0.52 MAP). We find that features describing users and the relationships between them are discriminative for this task. After the talk, we will carry out some examples on the collection of online data ]]>
Online social networks support users in a wide range of activities, such as sharing information and making recommendations. In this talk, I will talk about a human-generated recommendations of friendship based on hashtags that emerged in Twitter around 2010. I will present a study of a large-scale corpus of human friendship recommendations, using a large corpus of tweets gathered over a 24 week period and involving a set of nearly 6 million users. I will show that these explicit recommendations had a measurable effect on the process of link creation, increasing the chance of link creation between two and three times on average, compared with a recommendation-free scenario. Also, ties created after such recommendations have up to 6% more longevity than other Twitter ties. Finally, I will talk about a supervised system that ranks our user-generated recommendations, surfacing the most valuable ones with high precision (0.52 MAP). We find that features describing users and the relationships between them are discriminative for this task. After the talk, we will carry out some examples on the collection of online data ]]> Wed, 16 Nov 2016 12:26:24 GMT /slideshow/an-analysis-of-humangenerated-friendship-recommendations/69091993 ruthgarcia4@slideshare.net(ruthgarcia4) An Analysis of Human-Generated Friendship Recommendations ruthgarcia4 Online social networks support users in a wide range of activities, such as sharing information and making recommendations. In this talk, I will talk about a human-generated recommendations of friendship based on hashtags that emerged in Twitter around 2010. I will present a study of a large-scale corpus of human friendship recommendations, using a large corpus of tweets gathered over a 24 week period and involving a set of nearly 6 million users. I will show that these explicit recommendations had a measurable effect on the process of link creation, increasing the chance of link creation between two and three times on average, compared with a recommendation-free scenario. Also, ties created after such recommendations have up to 6% more longevity than other Twitter ties. Finally, I will talk about a supervised system that ranks our user-generated recommendations, surfacing the most valuable ones with high precision (0.52 MAP). We find that features describing users and the relationships between them are discriminative for this task. After the talk, we will carry out some examples on the collection of online data <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ffproject-161116122624-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Online social networks support users in a wide range of activities, such as sharing information and making recommendations. In this talk, I will talk about a human-generated recommendations of friendship based on hashtags that emerged in Twitter around 2010. I will present a study of a large-scale corpus of human friendship recommendations, using a large corpus of tweets gathered over a 24 week period and involving a set of nearly 6 million users. I will show that these explicit recommendations had a measurable effect on the process of link creation, increasing the chance of link creation between two and three times on average, compared with a recommendation-free scenario. Also, ties created after such recommendations have up to 6% more longevity than other Twitter ties. Finally, I will talk about a supervised system that ranks our user-generated recommendations, surfacing the most valuable ones with high precision (0.52 MAP). We find that features describing users and the relationships between them are discriminative for this task. After the talk, we will carry out some examples on the collection of online data
An Analysis of Human-Generated Friendship Recommendations from Ruth Garcia Gavilanes
]]> 97 2 https://cdn.slidesharecdn.com/ss_thumbnails/ffproject-161116122624-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Discovering Culture in Social Media and a Brief Case of Collective Memory /slideshow/discovering-culture-in-social-media-and-a-brief-case-of-collective-memory/59065746 slideshare-160304104945
'Discovering Cultural Trails in Social Media and Collective Memory in Wikipedia', with speaker Dr Ruth Garc鱈a-Gavilanes, Oxford Internet Institute. As a computational social science researcher, Ruth is interested in understanding online footprints, utilizing/developing computational methods and leveraging big data. In this seminar Ruth will present two case studies in this field: a) a study of how ones action on Twitter (e.g., deciding when to post messages) is linked to ones culture (e.g., countrys Pace of Life) and b) a case study of how collective memories can be measured using Wikipedia articles related to aircraft incidents and accidents. ]]>
'Discovering Cultural Trails in Social Media and Collective Memory in Wikipedia', with speaker Dr Ruth Garc鱈a-Gavilanes, Oxford Internet Institute. As a computational social science researcher, Ruth is interested in understanding online footprints, utilizing/developing computational methods and leveraging big data. In this seminar Ruth will present two case studies in this field: a) a study of how ones action on Twitter (e.g., deciding when to post messages) is linked to ones culture (e.g., countrys Pace of Life) and b) a case study of how collective memories can be measured using Wikipedia articles related to aircraft incidents and accidents. ]]> Fri, 04 Mar 2016 10:49:45 GMT /slideshow/discovering-culture-in-social-media-and-a-brief-case-of-collective-memory/59065746 ruthgarcia4@slideshare.net(ruthgarcia4) Discovering Culture in Social Media and a Brief Case of Collective Memory ruthgarcia4 'Discovering Cultural Trails in Social Media and Collective Memory in Wikipedia', with speaker Dr Ruth Garc鱈a-Gavilanes, Oxford Internet Institute. As a computational social science researcher, Ruth is interested in understanding online footprints, utilizing/developing computational methods and leveraging big data. In this seminar Ruth will present two case studies in this field: a) a study of how ones action on Twitter (e.g., deciding when to post messages) is linked to ones culture (e.g., countrys Pace of Life) and b) a case study of how collective memories can be measured using Wikipedia articles related to aircraft incidents and accidents. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slideshare-160304104945-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> &#39;Discovering Cultural Trails in Social Media and Collective Memory in Wikipedia&#39;, with speaker Dr Ruth Garc鱈a-Gavilanes, Oxford Internet Institute. As a computational social science researcher, Ruth is interested in understanding online footprints, utilizing/developing computational methods and leveraging big data. In this seminar Ruth will present two case studies in this field: a) a study of how ones action on Twitter (e.g., deciding when to post messages) is linked to ones culture (e.g., countrys Pace of Life) and b) a case study of how collective memories can be measured using Wikipedia articles related to aircraft incidents and accidents.
Discovering Culture in Social Media and a Brief Case of Collective Memory from Ruth Garcia Gavilanes
]]> 381 6 https://cdn.slidesharecdn.com/ss_thumbnails/slideshare-160304104945-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Language, Twitter and Academic Conferences /slideshow/language-twitter-and-academic-conferences/52560107 presentationht15-150908230545-lva1-app6892
To what extent do people tweet in other languages beyond English? How do lingua groups interact with each other? Is there an effect of language over online user interaction? ]]>
To what extent do people tweet in other languages beyond English? How do lingua groups interact with each other? Is there an effect of language over online user interaction? ]]> Tue, 08 Sep 2015 23:05:45 GMT /slideshow/language-twitter-and-academic-conferences/52560107 ruthgarcia4@slideshare.net(ruthgarcia4) Language, Twitter and Academic Conferences ruthgarcia4 To what extent do people tweet in other languages beyond English? How do lingua groups interact with each other? Is there an effect of language over online user interaction? <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentationht15-150908230545-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> To what extent do people tweet in other languages beyond English? How do lingua groups interact with each other? Is there an effect of language over online user interaction?
Language, Twitter and Academic Conferences from Ruth Garcia Gavilanes
]]> 324 8 https://cdn.slidesharecdn.com/ss_thumbnails/presentationht15-150908230545-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 USER BEHAVIOR IN MICROBLOGS WITH A CULTURAL EMPHASIS /ruthgarcia4/user-behavior-in-microblogs-with-a-cultural-emphasis presentationslideshare-150301052849-conversion-gate02
PhD defense contributions: Providing a study of human generated recommendation on Twitter and its effect. Garc鱈a-Gavilanes et al. Follow My Friends This Friday! An Analysis of Human-generated Friendship Recommendations. SocInfo13 [Best paper award] Describing the evolution of user behavior over time regarding the content they generate. Garc鱈a-Gavilanes et al. Who are my Audiences? A Study of the Evolution of Target Audiences in Microblogs. SocInfo14 Describing differences and similarities of users across countries regarding the way people tweet and connect with others. Garc鱈a-Gavilanes et al. Microblogging without Borders: Differences and Similarities. Websci11. w/ Poblete et al. Do All Birds Tweet the Same? Characterizing Twitter Around the World. In CIKM11 Proposing how to combine anthropological studies of culture with large scale data. Correlating how and when people tweet with dimensions of national culture and pace of life Garc鱈a-Gavilanes et al. Cultural Dimensions in Twitter: Time, Individualism and Power. ICWSM13 [Honorable mention] Improving the prediction of the communication strength between users from different countries by taking into account several cultural and socio-economic indicators taken from diverse sources. Garc鱈a-Gavilanes et al. Twitter aint Without Frontiers: Economic, Social, and Cultural Boundaries in International Communication. CSCW14.]]>
PhD defense contributions: Providing a study of human generated recommendation on Twitter and its effect. Garc鱈a-Gavilanes et al. Follow My Friends This Friday! An Analysis of Human-generated Friendship Recommendations. SocInfo13 [Best paper award] Describing the evolution of user behavior over time regarding the content they generate. Garc鱈a-Gavilanes et al. Who are my Audiences? A Study of the Evolution of Target Audiences in Microblogs. SocInfo14 Describing differences and similarities of users across countries regarding the way people tweet and connect with others. Garc鱈a-Gavilanes et al. Microblogging without Borders: Differences and Similarities. Websci11. w/ Poblete et al. Do All Birds Tweet the Same? Characterizing Twitter Around the World. In CIKM11 Proposing how to combine anthropological studies of culture with large scale data. Correlating how and when people tweet with dimensions of national culture and pace of life Garc鱈a-Gavilanes et al. Cultural Dimensions in Twitter: Time, Individualism and Power. ICWSM13 [Honorable mention] Improving the prediction of the communication strength between users from different countries by taking into account several cultural and socio-economic indicators taken from diverse sources. Garc鱈a-Gavilanes et al. Twitter aint Without Frontiers: Economic, Social, and Cultural Boundaries in International Communication. CSCW14.]]> Sun, 01 Mar 2015 05:28:49 GMT /ruthgarcia4/user-behavior-in-microblogs-with-a-cultural-emphasis ruthgarcia4@slideshare.net(ruthgarcia4) USER BEHAVIOR IN MICROBLOGS WITH A CULTURAL EMPHASIS ruthgarcia4 PhD defense contributions: Providing a study of human generated recommendation on Twitter and its effect. Garc鱈a-Gavilanes et al. Follow My Friends This Friday! An Analysis of Human-generated Friendship Recommendations. SocInfo13 [Best paper award] Describing the evolution of user behavior over time regarding the content they generate. Garc鱈a-Gavilanes et al. Who are my Audiences? A Study of the Evolution of Target Audiences in Microblogs. SocInfo14 Describing differences and similarities of users across countries regarding the way people tweet and connect with others. Garc鱈a-Gavilanes et al. Microblogging without Borders: Differences and Similarities. Websci11. w/ Poblete et al. Do All Birds Tweet the Same? Characterizing Twitter Around the World. In CIKM11 Proposing how to combine anthropological studies of culture with large scale data. Correlating how and when people tweet with dimensions of national culture and pace of life Garc鱈a-Gavilanes et al. Cultural Dimensions in Twitter: Time, Individualism and Power. ICWSM13 [Honorable mention] Improving the prediction of the communication strength between users from different countries by taking into account several cultural and socio-economic indicators taken from diverse sources. Garc鱈a-Gavilanes et al. Twitter aint Without Frontiers: Economic, Social, and Cultural Boundaries in International Communication. CSCW14. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentationslideshare-150301052849-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> PhD defense contributions: Providing a study of human generated recommendation on Twitter and its effect. Garc鱈a-Gavilanes et al. Follow My Friends This Friday! An Analysis of Human-generated Friendship Recommendations. SocInfo13 [Best paper award] Describing the evolution of user behavior over time regarding the content they generate. Garc鱈a-Gavilanes et al. Who are my Audiences? A Study of the Evolution of Target Audiences in Microblogs. SocInfo14 Describing differences and similarities of users across countries regarding the way people tweet and connect with others. Garc鱈a-Gavilanes et al. Microblogging without Borders: Differences and Similarities. Websci11. w/ Poblete et al. Do All Birds Tweet the Same? Characterizing Twitter Around the World. In CIKM11 Proposing how to combine anthropological studies of culture with large scale data. Correlating how and when people tweet with dimensions of national culture and pace of life Garc鱈a-Gavilanes et al. Cultural Dimensions in Twitter: Time, Individualism and Power. ICWSM13 [Honorable mention] Improving the prediction of the communication strength between users from different countries by taking into account several cultural and socio-economic indicators taken from diverse sources. Garc鱈a-Gavilanes et al. Twitter aint Without Frontiers: Economic, Social, and Cultural Boundaries in International Communication. CSCW14.
USER BEHAVIOR IN MICROBLOGS WITH A CULTURAL EMPHASIS from Ruth Garcia Gavilanes
]]> 979 4 https://cdn.slidesharecdn.com/ss_thumbnails/presentationslideshare-150301052849-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Who are my Audiences? Evolution of Target Audiences in Microblogs /slideshow/who-are-my-audiences-evolution-of-target-audiences-in-microblogs/41514252 presentation-141113093128-conversion-gate01
User behavior in online social media is not static, it evolves through the years. In Twitter, we have witnessed a maturation of its platform and its users due to endogenous and exogenous reasons. While the research using Twitter data has expanded rapidly, little work has studied the change/evolution in the Twitter ecosystem itself. In this pa- per, we use a taxonomy of the types of tweets posted by around 4M users during 10 weeks in 2011 and 2013. We classify users according to their tweeting behavior, and nd 5 clusters for which we can associate a different dominant tweeting type. Furthermore, we observe the evolution of users across groups between 2011 and 2013 and interesting insights such as the decrease in conversations and increase in URLs sharing. Our findings suggest that mature users evolve to adopt Twitter as a news media rather than a social network.]]>
User behavior in online social media is not static, it evolves through the years. In Twitter, we have witnessed a maturation of its platform and its users due to endogenous and exogenous reasons. While the research using Twitter data has expanded rapidly, little work has studied the change/evolution in the Twitter ecosystem itself. In this pa- per, we use a taxonomy of the types of tweets posted by around 4M users during 10 weeks in 2011 and 2013. We classify users according to their tweeting behavior, and nd 5 clusters for which we can associate a different dominant tweeting type. Furthermore, we observe the evolution of users across groups between 2011 and 2013 and interesting insights such as the decrease in conversations and increase in URLs sharing. Our findings suggest that mature users evolve to adopt Twitter as a news media rather than a social network.]]> Thu, 13 Nov 2014 09:31:28 GMT /slideshow/who-are-my-audiences-evolution-of-target-audiences-in-microblogs/41514252 ruthgarcia4@slideshare.net(ruthgarcia4) Who are my Audiences? Evolution of Target Audiences in Microblogs ruthgarcia4 User behavior in online social media is not static, it evolves through the years. In Twitter, we have witnessed a maturation of its platform and its users due to endogenous and exogenous reasons. While the research using Twitter data has expanded rapidly, little work has studied the change/evolution in the Twitter ecosystem itself. In this pa- per, we use a taxonomy of the types of tweets posted by around 4M users during 10 weeks in 2011 and 2013. We classify users according to their tweeting behavior, and 鐃nd 5 clusters for which we can associate a different dominant tweeting type. Furthermore, we observe the evolution of users across groups between 2011 and 2013 and 鐃interesting insights such as the decrease in conversations and increase in URLs sharing. Our findings suggest that mature users evolve to adopt Twitter as a news media rather than a social network. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-141113093128-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> User behavior in online social media is not static, it evolves through the years. In Twitter, we have witnessed a maturation of its platform and its users due to endogenous and exogenous reasons. While the research using Twitter data has expanded rapidly, little work has studied the change/evolution in the Twitter ecosystem itself. In this pa- per, we use a taxonomy of the types of tweets posted by around 4M users during 10 weeks in 2011 and 2013. We classify users according to their tweeting behavior, and 鐃nd 5 clusters for which we can associate a different dominant tweeting type. Furthermore, we observe the evolution of users across groups between 2011 and 2013 and 鐃interesting insights such as the decrease in conversations and increase in URLs sharing. Our findings suggest that mature users evolve to adopt Twitter as a news media rather than a social network.
Who are my Audiences? Evolution of Target Audiences in Microblogs from Ruth Garcia Gavilanes
]]> 2755 4 https://cdn.slidesharecdn.com/ss_thumbnails/presentation-141113093128-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Follow My Friends This Friday! An Analysis of Human-generated Friendship Recommendations /slideshow/presentation-29045321/29045321 presentation-131209125021-phpapp02
In Twitter, the hashtag #FF, or #FOLLOWFRIDAY, arose as a popular convention for users to create contact recommendations for others. Hitherto, there has not been any quantitative study of the effect of such human-generated recommendations. This paper is the first study of a large-scale corpus of human friendship recommendations based on such hashtags, using a large corpus of recommendations gathered over a 24 week period and involving a set of nearly 6 million users. We show that these explicit recommendations have a measurable effect on the process of link creation, increasing the chance of link creation between two and three times on average, compared with a recommendation-free scenario. Also, ties created after such recommendations have up to 6\% more longevity than other Twitter ties. Finally, we build a supervised system to rank user-generated recommendations, surfacing the most valuable ones with high precision ($0.52$ MAP), and we find that features describing users and the relationships between them, are discriminative for this task. ]]>
In Twitter, the hashtag #FF, or #FOLLOWFRIDAY, arose as a popular convention for users to create contact recommendations for others. Hitherto, there has not been any quantitative study of the effect of such human-generated recommendations. This paper is the first study of a large-scale corpus of human friendship recommendations based on such hashtags, using a large corpus of recommendations gathered over a 24 week period and involving a set of nearly 6 million users. We show that these explicit recommendations have a measurable effect on the process of link creation, increasing the chance of link creation between two and three times on average, compared with a recommendation-free scenario. Also, ties created after such recommendations have up to 6\% more longevity than other Twitter ties. Finally, we build a supervised system to rank user-generated recommendations, surfacing the most valuable ones with high precision ($0.52$ MAP), and we find that features describing users and the relationships between them, are discriminative for this task. ]]> Mon, 09 Dec 2013 12:50:20 GMT /slideshow/presentation-29045321/29045321 ruthgarcia4@slideshare.net(ruthgarcia4) Follow My Friends This Friday! An Analysis of Human-generated Friendship Recommendations ruthgarcia4 In Twitter, the hashtag #FF, or #FOLLOWFRIDAY, arose as a popular convention for users to create contact recommendations for others. Hitherto, there has not been any quantitative study of the effect of such human-generated recommendations. This paper is the first study of a large-scale corpus of human friendship recommendations based on such hashtags, using a large corpus of recommendations gathered over a 24 week period and involving a set of nearly 6 million users. We show that these explicit recommendations have a measurable effect on the process of link creation, increasing the chance of link creation between two and three times on average, compared with a recommendation-free scenario. Also, ties created after such recommendations have up to 6\% more longevity than other Twitter ties. Finally, we build a supervised system to rank user-generated recommendations, surfacing the most valuable ones with high precision ($0.52$ MAP), and we find that features describing users and the relationships between them, are discriminative for this task. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-131209125021-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In Twitter, the hashtag #FF, or #FOLLOWFRIDAY, arose as a popular convention for users to create contact recommendations for others. Hitherto, there has not been any quantitative study of the effect of such human-generated recommendations. This paper is the first study of a large-scale corpus of human friendship recommendations based on such hashtags, using a large corpus of recommendations gathered over a 24 week period and involving a set of nearly 6 million users. We show that these explicit recommendations have a measurable effect on the process of link creation, increasing the chance of link creation between two and three times on average, compared with a recommendation-free scenario. Also, ties created after such recommendations have up to 6\% more longevity than other Twitter ties. Finally, we build a supervised system to rank user-generated recommendations, surfacing the most valuable ones with high precision ($0.52$ MAP), and we find that features describing users and the relationships between them, are discriminative for this task.
Follow My Friends This Friday! An Analysis of Human-generated Friendship Recommendations from Ruth Garcia Gavilanes
]]> 470 4 https://cdn.slidesharecdn.com/ss_thumbnails/presentation-131209125021-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Twitter: Time, Individualism and Power /slideshow/twitter-time-individualism-and-power/24070513 presentation-130709150232-phpapp02
Is the culture of a country associated with the way people use Twitter? The answer is a definite Yes! We analyzed more than 2.34 million geo-located user profiles in 30 countries plus their tweets for 10 weeks Blogpost: http://crowdresearch.org/blog/?p=6767 Paper:http://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6102]]>
Is the culture of a country associated with the way people use Twitter? The answer is a definite Yes! We analyzed more than 2.34 million geo-located user profiles in 30 countries plus their tweets for 10 weeks Blogpost: http://crowdresearch.org/blog/?p=6767 Paper:http://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6102]]> Tue, 09 Jul 2013 15:02:32 GMT /slideshow/twitter-time-individualism-and-power/24070513 ruthgarcia4@slideshare.net(ruthgarcia4) Twitter: Time, Individualism and Power ruthgarcia4 Is the culture of a country associated with the way people use Twitter? The answer is a definite Yes! We analyzed more than 2.34 million geo-located user profiles in 30 countries plus their tweets for 10 weeks Blogpost: http://crowdresearch.org/blog/?p=6767 Paper:http://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6102 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-130709150232-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Is the culture of a country associated with the way people use Twitter? The answer is a definite Yes! We analyzed more than 2.34 million geo-located user profiles in 30 countries plus their tweets for 10 weeks Blogpost: http://crowdresearch.org/blog/?p=6767 Paper:http://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6102
Twitter: Time, Individualism and Power from Ruth Garcia Gavilanes
]]> 1138 5 https://cdn.slidesharecdn.com/ss_thumbnails/presentation-130709150232-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Cikm2011 doallbirdstweetthesame /slideshow/cikm2011-doallbirdstweetthesame/15154616 cikm2011doallbirdstweetthesame-121113073003-phpapp02
CIKM Presentation on 2011]]>
CIKM Presentation on 2011]]> Tue, 13 Nov 2012 07:30:02 GMT /slideshow/cikm2011-doallbirdstweetthesame/15154616 ruthgarcia4@slideshare.net(ruthgarcia4) Cikm2011 doallbirdstweetthesame ruthgarcia4 CIKM Presentation on 2011 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cikm2011doallbirdstweetthesame-121113073003-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> CIKM Presentation on 2011
Cikm2011 doallbirdstweetthesame from Ruth Garcia Gavilanes
]]> 212 2 https://cdn.slidesharecdn.com/ss_thumbnails/cikm2011doallbirdstweetthesame-121113073003-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-ruthgarcia4-48x48.jpg?cb=1571670048 Data Science www.ruthygarcia.com https://cdn.slidesharecdn.com/ss_thumbnails/rgpresentationdatasciencesummit2018meetup-180926100922-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/using-simple-machine-learning-models-in-a-new-ads-manager-116658995/116658995 Using Simple Machine L... https://cdn.slidesharecdn.com/ss_thumbnails/rgpresentationdssummit2018-180914074055-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/using-simple-machine-learning-models-in-a-new-ads-manager/114426333 Using Simple Machine L... https://cdn.slidesharecdn.com/ss_thumbnails/rgpresentationndrjune2018pdf-180619085349-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/using-machine-learning-in-the-delivery-of-ads/102643380 Using Machine Learning...